Method and apparatus for an inter-cell shortest communication

ABSTRACT

Novel method and system for distributed database ray-tracing is presented, based on modular mapping of scene-data among processors. Its inherent properties include matching between geographical proximity in the scene with communication proximity between processors.

CROSS-REFERENCE TO RELATED CASES

The present application is a Continuation in Part of US patent application Ser. No. 14/585,495 filed on Dec. 30, 2014 entitled “Efficient Inter-processor Communication in Ray Tracing”, which is a Continuation in Part of U.S. patent application Ser. No. 13/726,763 filed on Dec. 26, 2012 entitled “Method and Apparatus for Interprocessor Communication Employing Modular Space Division”, which is a Continuation of U.S. Pat. No. 8,373,699 filed on Apr. 16, 2012 entitled “Method and Apparatus for Parallel Ray-Tracing Employing Modular Space Division”, which is a Divisional of U.S. Pat. No. 8,189,001 filed on Dec. 24, 2010 entitled “Method and Apparatus for Parallel Ray-Tracing Employing Modular Space Division”, which claims priority to the U.S. Provisional Application No. 61/292,152 filed Jan. 4, 2010 entitled “Method and Apparatus for Parallel Ray-Tracing Employing Modular Space Division”; and also claims priority to U.S. Provisional Application No. 61/282,241 filed Jan. 6, 2010 entitled “Method and Apparatus for Parallel Ray-Tracing Employing Modular Space Division”; and U.S. Provisional Application No. 61/347,380 filed May 22, 2010 entitled “Method and Apparatus for Parallel Ray-Tracing Employing Modular Data Structure”; and U.S. Provisional Application No. 61/422,100 filed Dec. 10, 2010 entitled “Method and System for Dynamic Load Balancing of Data Parallel Processing,” all of which are hereby incorporated by reference.

FIELD OF THE INVENTION

The present invention relates generally to solving data-parallel processing and, more particularly, to data-parallel ray tracing technology enabling real time applications and highly photo-realistic images.

BACKGROUND OF THE INVENTION

Ray-tracing is a technique for generating images by simulating the behavior of light within a three-dimensional scene by typically tracing light rays from the camera into the scene, as depicted in FIG. 1A. In general two types of rays are used. Primary rays are traced from a particular point on the camera image plane (a pixel) into the scene, until they hit a surface, at a so-called hit or intersection point. Shadow and secondary rays are traced from a hit point to determine how it is lit. Finally, to determine how the surface material appears texture lookups and shading computations are performed at or near the hit point. FIG. 1B shows a scene having three objects and single light source. Three ray generations are created when the primary ray spawns other rays (N′ surface normal, R′ reflected ray, L′ shadow ray, T′ transmitted (refracted) ray).

Ray tracing is a high computationally expensive algorithm. Fortunately, ray tracing is quite easy to parallelize. The contribution of each ray to the final image can be computed independently from the other rays. For this reason, there has been a lot of effort put into finding the best parallel decomposition for ray tracing.

There are two main approaches to parallel ray-tracing in prior art: (i) ray-parallel, in which rays are distributed among parallel processors, while each processor traces a ray all the way, and (ii) data-parallel, in which the scene is distributed among multiple processors, while a ray is handled by multiple processors in a row.

The ray-parallel implementation of ray tracing would simply replicate all the data with each processor and subdivide the screen into a number of disjunct regions. Each processor then renders a number of regions using the unaltered sequential version of the ray tracing algorithm, until the whole image is completed. Whenever a processor finishes a region, it asks the master processor for a new task. This is also called the demand driven approach, or an image space subdivision. Load balancing is achieved dynamically by sending new tasks to processors that have just become idle. However, if a very large models need to be rendered, the scene data have to be distributed over the memories, because the local memory of each processor is not large enough to hold the entire scene. Then demand driven approach suffers from massive copies and multiplications of geometric data.

Data-parallel is a different approach to rendering scenes that do not fit into a single processor's memory. Here, the object data is distributed over the processors. Each processor owns only a subset of the database and it traces rays only when they pass through its own subspace. Its high data locality excludes massive moves of data, answering the needs of very large models. However, rendering cost per ray and the number of rays passing through each subset of database are likely to vary (e.g. hot spots are caused by viewpoints and light sources), leading to severe load imbalances, a problem which is difficult to solve either with static or dynamic load balancing schemes. Efficiency thus tends to be low in such systems.

In order to exploit locality between data accesses as much as possible, usually some spatial subdivision is used to decide which parts of the scene are stored with which processor. In its simplest form, the data is distributed according to a uniform distribution. Each processor will hold one or more equal sized cells. Having just one cell per processor allows the data decomposition to be nicely mapped onto a 3D grid topology. However, since the number of objects may vary dramatically from cell to cell, the cost of tracing a ray through each of these cells will vary and therefore this approach may lead to severe load imbalances.

A second, and more difficult problem to address, is the fact that the number of rays passing through each cell is likely to vary. Certain parts of the scene attract more rays than other parts. This has mainly to do with the view point and the location of the light sources. Both the variations in cost per ray and the number of rays passing through each cell indicate that having multiple cells per processor is a good option, as it is likely to balance the workload, albeit at the cost of extra communication.

The way the data is distributed over processors has a strong impact on how well the system performs. The more even the workload associated with a particular data distribution, the less idle time is to be expected. Three main criteria need to be observed for such distributions to lead to efficient execution of the parallel algorithm (Salmon and Goldsmith):

-   -   The memory overhead for each processor should be as equal as         possible     -   Communication requirements during rendering need to be minimized     -   Processing time for each processor needs to be equalized

Generating data distributions which adhere to all three criteria is a difficult problem, which currently remains unsolved in prior art. Most data distributions are limited to equalizing the memory overhead for each processor. This is a relatively simple exercise, because generating an adaptive spatial subdivision, such as an octree or KD-tree, gives sufficient clues as to which regions of space contain how many objects.

Another problem in ray tracing is the high processing cost of acceleration structures. For each frame, a rendering system must find the intersection points between many rays and many polygons. The cost of testing each ray against each polygon is prohibitive, so such systems typically use accelerating structures (such as Uniform grid, Octree, KD-tree, other binary trees, bounding boxes, etc.) to reduce the number of ray/polygon intersection tests that must be performed. As the data is sorted over space with the acceleration structure, the data distribution over the processors is based on this structure as well. The spatial subdivision is also used to establish which data needs to be fetched from other processors. Moreover, construction of optimized structures is expensive and does not allow for rebuilding the accelerating structure every frame to support for interactive ray-tracing of large dynamic scenes. The construction times for larger scenes are very high and do not allow dynamic changes.

There has been an attempt in prior art to lower the cost and complexity of acceleration structures by using its simplest form, where the data is distributed uniformly. Each processor will hold one or more equal sized cells. Having just one cell per processor allows the data decomposition to be nicely mapped onto a 3D grid topology. However, since the number of objects may vary dramatically from cell to cell, the cost of tracing a ray through each of these cells will vary and therefore this approach leads to severe load imbalances, and consequently the uniform distribution has been abandoned.

Today, the most popular data structure in prior art is the KD-tree. Ray traversal in a KD-tree is less efficient than in uniform grid. However, it is good for the scenes with non-uniform distribution of objects. The massive traversal of accelerating structure based on KD-tree typically consumes major chunk of the frame time (e.g. 63% in average, Venkatraman et al.). Thus, there is a great need in the art to devise a method of improved load balancing leaned on simple acceleration structure, such as uniform grid.

SUMMARY AND OBJECT OF THE PRESENT INVENTION

The present invention is a novel method and system for distributed database ray tracing, based on data-parallel approach and new modular mapping of the scene data into uniform grid. Due to modular distribution of the scene-data among processors the geographical neighborhood between cells translates to communication neighborhood of cell-designated processors on the ring, allowing task migration between neighboring processors for dynamic load-balancing and efficient data exchange. In preferred embodiment system the ray and scene data transfers are resolved by neighbor-based data exchange using mutual cache-memory access, instead of inter-processor communication, highly improving system's utilization. No global communication is needed.

The method can be implemented as well on commodity architectures, based on off-the-shelf elements, such as multicore chips, and others. It is understood that other embodiments of the present invention will become readily apparent to those skilled in the art from the following detailed description, wherein are shown and described only embodiments of the invention by way of illustration. As will be realized, the invention is capable of other and different embodiments and its several details are capable of modification in various other respects, all without departing from the scope of the present invention as defined by the claims. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not as restrictive.

The above summary is not an exhaustive list of all aspects of the present invention. Indeed, the inventor contemplates that his invention includes all systems and methods that can be practiced from all suitable combinations and derivatives of the various aspects summarized above, as well as those disclosed in the detailed description below and particularly pointed out in the claims filed with the application. Such combinations have particular advantages not specifically recited in the above summary.

Accordingly, a primary object of the present invention is to provide a new and improved method of and apparatus for practicing data-parallel ray tracing processes in multiprocessing based computer systems, while avoiding the shortcomings and drawbacks associated with prior art apparatus and methodologies.

Another object of the present invention is to provide a new and improved method of and apparatus for practicing data-parallel ray tracing processes having increasing efficiency in very large models.

Another object of the present invention is to provide a new and efficient method of ray tracing leaned on simple acceleration structure, such as uniform grid.

Another object of the present invention is to provide an improved method of ray tracing based on uniform grid acceleration structure, to avoid expensive traversals of complex data structures.

Another object of the present invention is to provide a new method of load balancing in conjunction with simple acceleration structure, such as uniform grid.

Another object of the present invention is to provide modular mapping (MM) of scene data into three-dimensional set of uniform grid of cells (i.e. cells), for parallel ray tracing.

Another object of the present invention is to provide modular distribution of scene data for parallel ray tracing, onto three-dimensional uniform grid of cells (i.e. cells) according to the method of modular mapping (MM), while the scene can be of any shape used in prior-art ray tracing.

Another object of the present invention is to provide an allocation of cells of scene data to processing elements (PEs) according to MM skewed alignment, having multiple cells per processor uniformly scattered across the scene.

Another object of the present invention is to provide an allocation of cells of scene data to processing elements (PEs) according to MM skewed alignment, having equivalence between geographical neighborhoods of cells of data with computational neighborhoods of PEs.

Another object of the present invention is to provide an allocation of cells of scene data to processing elements (PEs) according to MM skewed alignment, such that the shortest geographical distance between cells is translated into shortest inter-processor distance.

Another object of the present invention is to provide improved architecture for data-parallel ray tracing in the form of a multiple processing elements (PEs) based system, being connected by ring network topology, wherein every two neighboring PEs share a cache memory to accommodate fast ray transfer between neighboring cells of data, with no need of communication.

Another object of the present invention is to provide such apparatus for practicing data-parallel ray tracing in the form of a multiple processing elements (PEs) based system, being potentially implemented on different commodity multi-core architectures.

Another object of the present invention is to provide apparatus for practicing data-parallel ray tracing in the form of a multiple processing elements (PEs) based system having reduced communication needs due to modular mapped (MM) data structure of scene, avoiding any form of global communication.

Another object of the present invention is to provide apparatus for practicing data-parallel ray tracing in the form of a multiple PEs based system, having minimize massive ray migration among processors due to modular mapped (MM) data structure of scene.

These and other objects of the present invention will become apparent hereinafter and in the claims to invention.

BRIEF DESCRIPTION OF DRAWINGS

The invention is herein described, by way of non-limiting examples only, with reference to the accompanying figures and drawings, wherein like designations denote like elements. Understanding that these drawings only provide information concerning typical embodiments of the invention and are not therefore to be considered limiting in scope:

FIG. 1A. Prior art. The figure illustrates a setup of a ray-traced scene including view point, image and scene object. Reflection, refraction, and shadow rays are spawned from a point of intersection between primary ray and scene object.

FIG. 1B. Prior art. Another setup of a ray traveling across the scene is shown, having three objects and single light source. Three ray generations are created when the primary ray spawns other rays (N′ surface normal, R′ reflected ray, L′ shadow ray, T′ transmitted (refracted) ray).

FIG. 2A depicts an exemplary scene universe of N=4 according to the modular mapping scheme. (a) The modular mapping (MM) of scene data into three-dimensional set of 4×4×4 uniform grid of cells. Enumeration of cells is shown.

FIG. 2B depicts affiliation of cell to modules, and alignment of those modules.

FIG. 2C illustrates that each one of the 4 processors in a given example is assigned a module of 4×4 cells.

FIG. 2D exemplifies the skewed pattern of the modular set of cells k=3, scattered over the space.

FIG. 3A shows the neighborhood according to the modular mapping scheme. Cell 111, belonging to module k=3, is surrounded by 6-connected adjacent cells.

FIG. 3B shows the neighborhood modular rule of the modular mapping scheme. All the 6-connected neighbors belong to one of the two modules, k−1(module 2) and k+1 (module 0).

FIG. 3C depicts the computational neighborhood of processing elements assigned to the cells. The k-th PE assigned to the central cell has direct access to the data of all its 6-connected surrounding cells, all residing in the two adjacent memories of k−1 and k+1 PEs.

FIG. 4A shows the second degree of neighborhood according to the modular mapping scheme. Cell 111, on top of the above 6-connected cells, is second degree surrounded by 12-connected cells.

FIG. 4B shows that all second degree 12-connected cells belong to modules k, k−2 and k+2.

FIG. 4C depicts the associated processors. All the data of 12-connected cells is stored in the distance of k±2 from the central processor.

FIG. 5A depicts an extremely imbalanced scene of “Teapot-in-a-stadium.” For a traditional uniform grid subdivision the tracing result would be very inefficient.

FIG. 5B depicts the teapot-in-a-stadium scene resolved in the present invention by efficient ‘borrowing’ mechanism. The only object falls in a “central” cell belonging to module k, creating an extreme load imbalance.

FIG. 5C depicts load balancing of the teapot-in-a-stadium scene by “borrowing” two adjacent processors in a distance of single hop across the ring communication.

FIG. 5D depicts extended load balancing of the teapot-in-a-stadium scene by “borrowing” four processors in a distance of two hops across the ring communication.

FIG. 6 shows a simple path of a traced ray within the uniform universe.

FIG. 7A illustrates a tracing example of family of rays.

FIG. 7B summarizes the relation between involved cells and PEs in the tracing example.

FIG. 7C depicts the consequent inter-PEs communication while tracing the ray family. The entire data transfer occurs solely between PE4 and its two neighbors, PE3 and PE5.

FIG. 8A depicts the PE-memory resident data structure associated with each cell.

FIG. 8B depicts the PE cache resident PE data.

FIG. 8C depicts cell's data structure.

FIG. 9A depicts an elaborated example of ray tracing in the modularly mapped scene.

FIG. 9B depicts detailed ray tracing process at specific PE and specific cell, of the elaborated example.

FIG. 9C shows the folding stage of the exemplary rays, when the light values are aggregated.

FIG. 10A depicts a high level flowchart of ray tracing process of present invention.

FIG. 10B depicts a flowchart of ray tracing process in a PE.

FIG. 10C depicts a flowchart of the folding stage of ray tracing process.

FIG. 10D depicts a flowchart of processing a ray.

FIG. 11A depicts the preferred embodiment of present invention comprising a multi-core system on chip (SOC).

FIG. 11B depicts one PE with access mechanism to neighboring PEs and to communication ring.

FIG. 12 depicts an embodiment which is based on commodity device; Larrabee multicore by Intel.

FIG. 13 depicts an embodiment which is based on commodity device; Cell multicore by IBM.

DETAILED DESCRIPTION OF THE ILLUSTRATIVE EMBODIMENTS OF THE PRESENT INVENTION

Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification discussions, utilizing terms such as “processing”, “computing”, “calculating”, “generating”, “creating” or the like, refer to the action and/or processes of a computer or computing system, or processor or similar electronic computing device, that manipulate and/or transform data represented as physical, such as electronic, quantities within the computing system's registers and/or memories into other data, similarly represented as physical quantities within the computing system's memories, registers or other such information storage, transmission or display devices.

Embodiments of the present invention may use terms such as processor, computer, apparatus, system, sub-system, module, processing element (PE), multicore, FPGA, GPU and device (in single or plural form) for performing the operations herein. This may be specially constructed for the desired purposes, or it may contain a general purpose computer selectively activated or reconfigured by a computer program stored in the computer. Several technical terms which are specifically associated with our disclosure are herein defined.

Object—a scene is made up of objects. An object can stand for a 2D or 3D primitive (polygon, triangle, solid, etc.), or a complex object made up of primitives. ‘Pixel’ as a 1D entity, is not an object.

Cell—a sub-scene element capable to contain objects. It is different from ‘voxel’ that contains a single volumetric value.

Empty cell—is a cell without objects, as opposed to a data-filled cell or polygon populated cell.

Hit point—a point where a ray intersects an object.

Visible object—is an object which is visible, at least in part, from the point of view. It is not fully hidden by other objects.

Load balancing—distributing workload across multiple processors to achieve optimal resource utilization, maximize throughput, minimize response time, and avoid overload.

Static load balance—All information is available to scheduling algorithm, which runs before a run time computation starts.

Shared memory system—parallel computing system having memory shared between all processing elements in a single address space.

Distributed memory system—parallel computing system in which each processing element has its own local address space.

Private memory—owned by a processing element and accessible by the owner.

Local objects—objects residing wholly or partly in a cell.

Hit point (HIP)—an intersection point between a traced ray and an object within the scene.

Shadow rays—rays that are traced from a hit point to light source in order to find out whether the point is lit or shadowed.

Bouncing rays—traced rays that bounce from the surface of an object (called also secondary rays).

Geographical neighborhood of cells—In a 3D grid of uniform cells, each cell has 6 cells (6-connected) in a direct geographical neighborhood.

Communication neighborhood—in a ring communication topology each processing element has two direct neighbors on the ring.

Asynchronous exchange of data—exchange of data between sender and receiver (e.g. two neighboring cells) in a random way, without an agreed-upon timing of the transfer, and not in a centrally controlled way.

Autonomous exchange of data—exchange of data from a sender-cell to a receiver-cell, performed by the processing element assigned to sender-cell, without needing to synchronize with other processing elements, or with a central control system.

The principles and operation of an apparatus according to the present invention may be understood with reference to the figures and the accompanying description wherein similar components appearing in different figures are denoted by identical reference numerals. The drawings and descriptions are conceptual only. In actual practice, a single component can implement one or more functions; alternatively, each function can be implemented by a plurality of components and devices. In the figures and descriptions, identical reference numerals indicate those components that are common to different embodiments or configurations. Identical numerical references (even in the case of using different suffix, such as 5, 5 a, 5 b and 5 c) refer to functions or actual devices that are either identical, substantially similar or having similar functionality. It will be readily understood that the components of the present invention, as generally described and illustrated in the figures herein, could be arranged and designed in a wide variety of different configurations. Thus, the following more detailed description of the embodiments of the apparatus, system, and method of the present invention, as represented in the figures herein, is not intended to limit the scope of the invention, as claimed, but is merely representative of embodiments of the invention.

The processes/devices (or counterpart terms specified above) and displays presented herein are not inherently related to any particular computer or other apparatus, unless specifically stated otherwise. Various general purpose or commodity computing systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct a more specialized apparatus to perform the desired method. The desired structure for a variety of these systems will appear in the description below. In addition, embodiments of the present invention are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the inventions as described herein.

Skewed Modular Mapping

In one aspect of the invention, the space is subdivided into uniform grid of cells according to Modular Mapping (MM) scheme. In general, any spatial subdivision in parallel ray tracing must have a threefold use; (i) data distribution over processors, (ii) acceleration structure, and (iii) means to know what data to be fetched from other processors. In addition, spatial subdivision must address the load imbalance problem. It will become apparent how the Modular Mapping subdivision of present invention, addresses all those requirements, despite its simplicity and low cost.

We divide the whole scene into Nx, Ny, and Nz intervals along x, y, and z axes, respectively, the three-dimensional scene is partitioned into Nx×Ny×Nz axis-aligned grid cells. To make the analysis of time and space complexity easier, we will assume Nx=Ny=Nz=N. The universal space is then partitioned into axis-parallel N3 cuboidal cells.

The cells are arranged in N modular subsets (or modules), such that each module comprises exactly N2 cells. N is also the number of processors, in a way that each processor, or cluster of processors, is allocated a module of N2 cells. Module k-th is the geometric location of all cells such that:

k=(x+y+z)mod N

An example is shown in FIG. 2A. A scene universe is divided into 4×4×4 equal cells. Each cell is enumerated according to its coordinates X, Y, Z. FIG. 2B depicts the same cells sorted by their modular affiliation. Notice the skewed alignment of modules. A cell belongs to the kth module by its location, according to the modular formula:

k=(x+y+z)mod 4

e.g. a cell at x=2, y=1, z=0 is assigned to module 3.

Data distribution over processors. All cells are distributed over at least N processors, such that each module is dominated by one processor, or cluster of processors. Although for simplicity, the following discussion refers to the case of a single processor per module. In way of example, the table below shows cell distribution over four processors, for the case of N=4.

X + Y + Z 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 PE 0 1 2 3 0 1 2 3 0 1 2 3 0 1 2 3

Because of this simply formulated corresponding of data distribution over processors, it is FIG. 2C shows schematically that each PE gets assigned exactly 16 cells. The scattering pattern of modular cells is apparent in FIG. 20. Module k=3 is shown, out of 4 modules. Its fairly uniform distribution across the space is a critical factor for effective load balancing.

In general, acceleration structures assist in reducing the number of ray/object intersection tests while looking for the hit point. In complex structures, such as KD-tree or Octree, it is done by traversing the data tree, which is a very expensive process. As opposed to it, ray tracing on the uniform grid of MM, is based on the well known incremental algorithm for line drawing on 3D raster grid (3D DDA). While tracing, the algorithm skips empty cells by only examining the farthest intersection point of the ray with the next non-empty cell.

Data fetched from other processors. In regular course of ray tracing according to present invention, for small data scenes the entire scene can be stored in a shared memory, or for large databases it has be distributed among processors' local memories. In the later case, a processor holding data of required x, y, z site can be easily located by any other processor by simply calculating the k value from the MM formula.

In one way the Load balancing method of present invention exploits the natural dispersion of modular cells across the space, as shown in FIG. 20. This is an intra-modular, or local load balancing. A processor (or duster of) to which all cells of a given module are allocated, prioritizes its attention according to cell's load. Moreover, a processor can handle multiple cells at a time by multi-threading. The other way is inter-modular load balancing, having a more global effect, carried out by processor “borrowing” mechanism. It is based on the MM feature of correlation between geographical and computational neighborhoods. It is used for offloading an overloaded processor by its closest neighboring processors. This method will become apparent in the following discussion.

Geographical Vs. Computational Neighborhood

An important feature of the Modular Mapping of the present invention is the similitude between geographical and computational neighborhood. First, the neighborhood rule of MM must be understood. We are going to analyze two circles of proximity to a cell; the first degree is of 6 closest wrapping around cells, and the second degree counting 18 cells (including the 6). In FIG. 3A a cell 1, 1, 1 is shown rounded by its 6-connected geographical neighboring cells. The way of mapping those cells onto modules is indicated in FIG. 3B. The central cell, belonging to module k, is wrapped by cells assigned to two modules only: k+1 and k−1. Because of the one-to-one mapping of module per processor, the meaning is that central processor PEk can access all the geographically surrounding data of every one of its modular cells, merely fetching two processors. And vice versa, the processors PEk−1 and PEk+1 can access all the data of PEk, not just for the central cell, but of all its modular cells. Moreover, in the simplest ring topology, as illustrated in FIG. 3C, the distance is of one hop only. The imperative fact is that the computational proximity matches the geographical one, thus the shortest geographical distance between cells is translated to the shortest inter-processor communication distance.

The second circle of proximity includes 18-connected cells. 12 of them, excluding the 6-connected, are shown in FIG. 4A. The way they are mapped onto modules is indicated in FIG. 4B. Two modules have joined the first ones; k−2 and k+2. All the four modules, assigned to processors are shown in FIG. 4C. The central processor PEk can access all the 18-connected geographical neighbors of any of its cells from four processors only, and vice versa, the processors PEk−1, PEk−2, PEk+1 and PEk−2 can access all the data of PEk, just in one or two ring hops.

It is noteworthy that in the bouncing stage of ray-tracing, majority of rays entering or leaving a cell communicate to “next door” cells, which due to the modular scheme is always done across a minimal communication distance between two neighboring processors on the ring. There is no requirement to exchange data between any two non-adjacent processors on the ring. As will become apparent, in the preferred embodiment of the present invention this minimal computational distance is cheaply hopped by a shared cache-memory access between adjacent PEs.

Inter-Modular Load Balancing

Spatial subdivision suffers from clustering of objects in a few regions for most of the scenes. In modular scale clustering can be overcome by intra-modular load balancing mechanism. However, for global scale clustering an inter-modular mechanism is vital, such that load distribution between modules can be adjusted. The global clustering is addressed by “borrowing” mechanism, based on the inexpensive data transfer between adjacent PEs. For an overloaded module, part of the load can be taken by an unemployed neighboring PE, which is borrowed to process some cell subset of the busy neighbor. The data associated with those cells must be delivered to the borrowed PE. This is readily accomplished by data transfer between adjacent PEs.

The borrowing mechanism is demonstrated on a known test case in the art; the “teapot-in-a-stadium” scene. This test scene has a very small object (the teapot) inside a very large empty space (the stadium). If a traditional uniform grid subdivision is used for this scene, the result is very inefficient due to extremely imbalanced load, as shown in FIG. 5A.

FIG. 5B depicts the scene in present invention's MM data domain. For clearness, only the 2D section is shown. The only (teapot) object falls in a “central” cell belonging to module k, creating an extreme load imbalance. The borrowing mechanism of present invention solves such imbalance by migrating parts of the cell's load to neighboring processors, PEk−1 and PEk+1, offloading the central PEk. Then the source data at cell k must be shared with destination processors. Due to the Modular Mapping, the minimal geographical distance to surrounding cells, correlates with the smallest physical distance between the memories of PEk with those of PEk−1 and PEk+1. This feature significantly lowers the cost of data moves in the case of borrowing. FIG. 5C shows the solution in processor domain. Processors k−1 and k+1 are “borrowed” to offload the central processor k. The relevant data residing at the k-th PE's memory must be delivered to the borrowed processors. Due to MM scheme this data is transferred across the ring between adjacent processors. No global communication is required.

An extended load balancing of the teapot-in-a-stadium scene can be done by mobilizing the second degree neighborhood as well. As shown in FIG. 5D, processors k−2, k−1, k+1 and k+2 are “borrowed” to offload the central processor k. The relevant data residing at the k-th PE's memory is transferred by utmost 2 hops across the ring. No global communication is required as well.

Tracing a Ray

FIG. 6 shows a very basic example of tracing a pathway from a particular point on the camera image plane (a pixel 601) into the scene, until it hits a surface at intersection point 602. The ray is created by PE at a front cells (facing the screen) of its module. The ray progression is based on the incremental 3D DDA algorithm for line drawing on 3D raster grid. Each module keeps a list of its empty cells. While tracing, the algorithm skips empty cells by only examining the farthest intersection point of the ray with the next non-empty cell. The intersection at each cell is tested against the accelerating structure of local KD-tree. At intersection point a shadow ray is created toward the source of light 603, testing whether the hit point is shadowed by any occluding object, and the primary ray splits into reflective (secondary) ray that hits another object 605. Assuming maximal recursion depth of a ray is limited to 2, the reflection ray terminates the trail. In its way back the secondary ray brings back light and texture values of its hit point 605, that are being summed-up at the cell of intersection 602, and the final value is returned to the screen's pixel 601.

In another example the dramatic cutback of inter-PE communication is demonstrated, an important aspect of present invention, by more elaborated case depicted in FIG. 7A. The scene is partitioned into regular grid of 8×8×8 cells, controlled by parallel array of 8 PEs, 64 cells per PE. A cell is enumerated by its X, Y, Z coordinates, e.g. cell 642 is positioned at X=6, Y=4, Z=2, and allocated to PE4 by its modular value. The path of the ray starts at a given pixel (a) 701, and intersects an object in cell 642 (b) 702, where a generation of three additional rays is created: a shadow ray that is sent to light source (f) 705, reflection ray directed to (c) 703 in cube 652, and transmission ray (d) 704 reaching cube 542. A farther generation is shown when the ray of 672 is reflected to cube 742 (e) 706. Additionally, from each point of intersection a shadow ray is sent to light source (f) 705. The table of FIG. 7B summarizes the relations between involved cells and PEs. The consequent inter-PEs communication while tracing the ray family is shown in FIG. 7C. The entire data transfer is limited to PE4 and its two neighbors, PE3 and PE5. Another data transfer, between cells 652 and 742 (c to e), needs no communication, since both cells are allocated to the same processor PE4.

The above example demonstrates how the inter-processor communication in data-parallel ray tracing system is reduced. Due to the Modular Mapping of present invention, the data exchange occurs always between two neighboring PEs on the ring-based communication, which is the shortest data exchange path. Never a longer exchange path, i.e. between two distant PEs along the ring, is required. Such a reduced communication ansures higher efficiency and improved performance. This embodiment will be readily understood.

Ray and Scene Data Structures

The following detailed description of the ray and data structures, as represented in the figures herein, is not intended to limit the scope of the invention, as claimed, but is merely representative of embodiments of the invention.

In the present invention, taking the data-parallel approach of ray tracing, the object data is distributed over the memories, gaining high locality when each processor relates to a subset of the database and traces segments of rays that pass through its subspace. Moreover, the exchange of data from a sender-cell to a receiver-cell, by the processing element assigned to the sender-cell, is performed locally, i.e. no need to synchronize with other processing elements, or with any central control system. This is an autonomous exchange of data.

Three main criteria need to be observed for such distributions to lead to efficient execution of the parallel algorithm:

-   -   Communication requirements during rendering need to be minimized     -   The memory overhead for each processor should be as equal as         possible     -   Processing time for each processor needs to be equalized

Communication requirements are minimized by high data locality excluding massive moves of data, moreover, the Modular Mapping of present invention transforms the massive and global communication needed for balancing the load, into neighbor-wise regularly patterned communication.

The amount of scene memory varies among processors because of the schematic data distribution, without trying to equally distribute the scene objects. However, locality of scene data minimizes its moves. Moreover, the use of cache-memories in the present invention is intended to nullify cache misses and memory fetches during cell processing, once cell data have been downloaded from memory to cache. All these keep the memory overhead low and fairly equal. The other data in the system is that of rays, it is non-massive and moves among processors in neighbor-wise fashion, accommodated by shared-cache transfers of the preferred embodiment, or by single ring hop for commodity based embodiment, still minimizing memory overhead. The exception is when empty cells must be skipped, and then ray may be transferred between non-adjacent processors.

Equalization of processing time in cases of global clustering is addressed by the “borrowing” mechanism, as described hereinabove. The data associated with borrowing is delivered by massive data transfers among adjacent PEs, by means of shared-cache transfers of preferred embodiment, or by single ring hop for commodity based embodiment.

The scene and ray data are structured to address the above three criteria. Processor's subspace consists of the entirety of module's cells, while the PE is competent of processing only one cell at a time (or few more by applying multithreading). Therefore a cell can be in an active state, handled by processor, or inactive state, waiting for its turn, and a mechanism is needed to keep all the inactive cells receiving rays from all over in real time. This and other special needs of present invention data handling are accomplished by differentiating between the duties of processor's memory and its cache. FIG. 8A-B depict the data structures of both. As shown in FIG. 8A, the processor's memory holds scene 812 and ray data 813 for all non-empty cells of the module, and list of empty cells 811. There are N cells per module, while M of them are non-empty. Cell's local scene 812 is KD-tree structured for intersections acceleration. The cache-memory in FIG. 8B holds the entire data set of the active cell k 822. In addition, the cache stores input buffers of all processor's cells 824, enabling a continuous ray reception mode in real time for all inactive cells (excluding the empty cells). The received rays will be taken care of when the cell turns active. Another important partition in cache is Transfer-buffer 821 to accommodate communicating the data between processor and its nearest neighbor.

A ray life cycle has two distinct phases: (i) building the ray family and associated nodes, and (ii) folding back the ray structure while aggregating the light value toward the final screen pixel. The build phase includes ray commencement at a screen's pixel, seeking intersection with object, constructing a node, and generating children rays of shadow, transmission and reflection. At this phase rays do not carry any light values. The second phase of back folding starts upon termination of rays, due to depth limitation or space limitation. All the family rays are returned back to their source nodes carrying light value which is summed up with other rays in the node. A node is being removed from Node list, right after all children have been aggregated into parent ray. The parent, in turn, is sent back to its source node, etc., until the entire family's light value finalizes at the screen pixel.

FIG. 8C details the structure of ray data for each non-empty cell. The data associated with life cycle of a ray is divided to static and dynamic elements. Static data resides in memory: Input-buffer (incoming rays), Output buffer (outgoing rays), Termination buffer (terminating rays), and Node Record defining the intersection point. The dynamic Ray Record, defining ray attributes, source and destination, is being conveyed among the cells. Each of the classes is farther break down and explained in FIG. 8C. Their exact functions will become clear in the hereinafter example.

The static data comprises:

Input-Buffer

-   -   Incoming rays     -   Local rays (created in course of local processing)     -   Folding rays (incoming ray on its way back)

Output-Buffer

-   -   immediate send (to be sent to target cell)     -   Return to source (to be returned to the cell it came from)

Termination Buffer

-   -   Parent rays (waiting for aggregation in Folding cycle)     -   Terminated rays (waiting for aggregation in Folding cycle)

Node Record

-   -   local node ID;     -   Pxl_ID;     -   Topology [parent (source_cell_ID), shadow (target_cell ID),         out_ray1 (target_cell); out_ray2 (target_cell];     -   Status: (build(0)/fold(1);     -   Light value         The dynamic data comprises:

Ray Record:

-   -   Pxl_ID;     -   Source (cell; node);     -   Ray class: regular(0)/shadow(1);     -   Status: Build(0)/Fold(1);     -   Ray's normal     -   Light value

The understanding of detailed structure of the Ray and Node records is required for the elaborated example hereinafter:

Ray Record

Pixel ID; source cell; source node (O=pixel, any number=node); regular (0) or shadow (1) ray; build (0) or fold (1) state; ray's normal; light value

Node Record:

Local node ID; Pixel ID; father (source cell); shadow (target cell or terminate(0)); child1 (target cell); child2 (target cell or terminate(0)); state (build, fold); light value

Detailed example of the ray handling mechanism is described in FIGS. 9A-9C. FIG. 9A shows the course of primary ray, secondary rays and shadow rays. This is a simplified test case where the space is partitioned into 4×4×4 cells, processed by parallel array of 4 PEs, 16 cells per PE. The exemplified cell is 133, processed by PE=3. The cell receives ray R1 which during the Build phase intersects with object 1, splits into three children: reflected ray R′1 which leaves the cell; transmitted ray T′1 which encounters another intersection inside the cell; and shadow ray L′1 which leaves the cell for the light source. The first point of intersection creates node 1. Second node within the cell is created where T′1 hits object 1 and splits into: R′2 that leaves the cell to adjacent one; T′2 that escapes out of the scene space; and shadow ray L′2 which leaves toward light source. The Folding phase occurs when rays start returning along with light values, which are summed up at the nodes. R′2, T′2 and L′2 aggregate into T′1 and node 2 is been removed from records. T′1, R′1 and L′1 aggregate into R1 which is sent back to the pixel, and finally node 1 is removed.

Now let's repeat step by step on the above scenario, the way it is performed by PE=3. First the Build phase.

-   1. In some prior time, when cell 133 where inactive, arrives in     Received Rays queue a ray having the following record: 1000000; 130;     0; 0; 0 normal value; 0. Ray's ID is 1000000, named after the source     pixel; the source cell is 130; It comes directly from the pixel     rather than some node; It is a regular ray (not a shadow); build     phase; Its direction given by normal value; light value is null. The     ray waits in the queue for the cell to become active. -   2. When the cell becomes active, its specific data is downloaded     from memory into PE's cache: (i) Cell_(—)133 KD-tree, (ii) static     ray data comprising buffers and node list. -   3. Node 1. The ray is taken from the queue for intersection test     against the Cell_(—)133 KD-tree. Intersection is found and     registered in a newly created Node 1: 1; 1000000; 130; 032; 133;     213; 0; 0. The ray is stored in Termination Buffer as Parent ray,     waiting for Folding phase. -   4. Node 1. Reflection ray is created and tested for possible     intersection with Cell_(—)133 KD-tree. Since internal intersection     was not found, another cell on ray's way is pierced by 3D DDA. The     ray record is assembled: 1000000; 133; 1; 0; 0; normal value; 0, and     sent to target cell 213. -   5. Node 1. Transmission ray is created and tested for possible     intersection with Cell_(—)133 KD-tree. Intersection is found and     registered in a newly created Node 2: 2; 1000000; 133; 032; 033; 0;     0; 0. The ray is stored in Termination Buffer as Father ray, waiting     for Folding phase. -   6. Node 1. Shadow ray is created; its direction toward light source     is calculated, and tested for possible intersection with Cell_(—)133     KD-tree. Once internal intersection was not found, another cell on     ray's way is pierced by 3D DDA. The ray record is assembled:     1000000; 133; 1; 1; 0; normal value; 0, and sent to target cell 032. -   7. Node 2. Reflection ray is created and tested for possible     intersection with Cell_(—)133 KD-tree. Once internal intersection     was not found, another cell on ray's way is pierced by 3D DDA. The     ray record is assembled: 1000000; 133; 2; 1; 0; normal value; 0, and     sent to target cell 033. -   8. Node 2. Transmission ray is created and tested for possible     intersection with Cell_(—)133 KD-tree. Since internal intersection     was not found, then 3D DDA founds out that the ray leaves the scene.     Therefore it is given a background light value (or texture) and     stored in Terminated Ray as: 1000000; 133; 2; 0; 0; normal value; 0.     Now it is ready to Folding phase, to be summed up into its parent     ray at Node 2. -   9. Node 2. Shadow ray is created, its direction toward light source     calculated, and tested for possible intersection with Cell_(—)133     KD-tree. Once internal intersection was not found, another cell on     ray's way is pierced by 3D DDA. The ray record is then assembled:     1000000; 133; 2; 1; 0; normal value; 0, and sent to target cell 032.

The Folding phase is shown in FIG. 9C.

-   1. There are rays waiting for folding when the cell becomes active     again: (i) in cell's Input-Buffer, which permanently resides in PEs     cache and continuously receives returning rays from previously     target cells, (ii) Parent rays in Termination Buffer. -   2. Node 2. The returning ray 1000000; 133; 2; 0; 1; normal value;     light value is summed up with the locally terminated ray 1000000;     133; 2; 0; 0; normal value; 0. The intermediate result is stored in     Node 2. Upon arrival of the shadow ray 1000000; 133; 2; 1; 1; normal     value; light value the aggregation is completed and stored in the     Light field of the parent ray 1000000; 133; 1; 0; 0; normal value;     light value. The parent is stored in Input Buffer for next folding     cycle, and Node 2 is removed from the list. -   3. Node 1. Upon arrival of the rays 1000000; 133; 1; 0; 1; normal     value; light value and 1000000; 033; 1; 1; 1; normal value; light     value, they are aggregated along with the awaiting ray 1000000; 133;     1; 0; 0; normal value; light value, into parent ray 1000000; 130; 0;     0; 1; normal value; light value and sent back to cell 130. The     record of Node 1 is removed from the list.

Data Locality

Assuming that the partition of universe was properly designed, such that the cache size is adequate for all cell's data, then during the cell activity period all its associated data resides in the cache: KD-tree, all its buffers and rays. This data entirely describes everything occurring in the cell, no additional data from external memory is needed. This locality of data eliminates cache misses during cell's processing time. The only cache miss is associated with assigning the processor to the successive cell. This can be eventually avoided if the cache is big enough to preload the successive data.

Flowchart

Flowchart of FIG. 10A describes the high level ray tracing process of present invention. A parallel array of any N number of PEs is assumed, while one of the PEs monitors the process at high level. It can be just one of the processors (e.g. PE=0), performing an extra task 1100 of calculating and broadcasting partitioning parameters to PEs, and merging the partial results of the PEs 1107-1108.

At first step of initialization phase, upon receiving the portioning parameters, each PE downloads its sub-partitioned scene data from central storage to PE's memory 1101. The data is analyzed to find the empty cells 1102 and to construct a KD-tree for the non-empty cells 1103. Then a set of buffers is initialized for every (non-empty) cell in PE's memory 1104. The Input buffers of all cells are copied to cache memory, for staying all the time in reception mode 1105. Then each processor starts the ray tracing phase 1106 (this stage is farther detailed in FIG. 10B).

Upon finalizing all partial pixel images at PEs, the monitoring PE merges them into final image 1107, and sends the image to display 1108.

The flowchart of FIG. 10B describes the ray tracing at each PE. First all the front cells, facing the screen image, are processed for generating rays. It is possible to process a bundle of rays, relying on their coherence. However, in the herein described ray tracing processes only a single ray is taken at a time, to simplify the principal description. A ray, after being generated 1011, starts the tracing process in the current front cell. This ray processing task 1012 is described elsewhere (FIG. 10C) in depth. If intersection was met in the front cell, then it was registered in ‘local rays’ of Input buffer for later processing. After all rays are generated pixels of current front cell 1013, and all front cells are exhausted 1014, the most loaded cell is chosen for processing 1019. This cell data KD-tree structured and ray structure are loaded to PE's cache 1020-1021, and next ray in the ‘Input List’ (‘received’) 1015 is taken for processing 1012. After all these rays are done, the locally generated rays in ‘Input Buffer’ (‘local’) 1016 are processed. When this is done, all the nodes waiting in ‘Termination Buffer’ of the current cell are being folded 1017. Upon terminating the cell, next heaviest cell is chosen, 1019 etc. When all “input Buffers' of all cells are done, the folding cycle 1018 terminates the tracing.

The detailed flowchart of ray processing is depicted in FIG. 10C. It starts by ray intersection with local KD-tree 1050. If no intersection detected, next cell along the ray is calculated 1065 by means of 3D DDA, and stored in ‘Output Buffer’ (‘Immediate send’). If intersection has been detected, and the intersecting ray is not a shadow ray, a new node is registered 1051, then shadow rays are created and stored 1052-1053, and the secondary rays of reflection 1054 and refraction 1055 are calculated and stored for later processing. In case it is a shadow ray, a proper handling is done 1056-1059.

The folding cycle is described in FIG. 10D. The next ray from ‘folding’ queue is taken, matched with a node 1031, light value aggregated 1032, and nodes are folded back 1034 until the pixel of origin is reached 1036.

Implementation Preferred Embodiment of Present Invention

In the present invention, by taking the data-parallel approach of parallel ray tracing the inter-processor communication is highly reduced. Due to the Modular Mapping there is no massive global communication, and most of the data exchange occurs between neighboring PEs. Because of the high importance of neighbor-wise communication, the preferred embodiment comprises shared memory access between adjacent processors, replacing wired communication, for higher efficiency and improved performance. The preferred embodiment, depicted in FIG. 11A, is a distributed memory parallel ray tracing system, where the scene data is distributed over the memories. There are some N processors, each processor 1101 includes CPU, cache and I/O, and is connected to private memory 1102. All processors are connected via ring 1105. In some cases the ring can be replaced by different topology, e.g. router, however this will deteriorate the efficiency and cost/performance). The neighbor-wise communication, which happens to be the most significant communication in present invention, is implemented by shared-memory means, via caches. Each processor 1101 is connected to one neighbor by its own cache 1103 and by direct connection to the second neighbor's cache 1104. All processors are having access to central storage 1106 via ring. The massive data moves, such as for inter-processor load-balancing (“borrowing”), and majority of ray data moving between neighboring cells, are accomplished via cache-based communication. Another ray communication, much less frequent, such as skipping over empty cells, is done via ring.

Another preferred embodiment, depicted in FIG. 11B, is a shared memory parallel ray tracing system, with no private memories. This kind of system is implementable by SOC (system on chip) technology (termed also ‘multicore’), where the entire system resides in a single chip, and silicon constraints exclude constructing private memories on chip. The central memory 1110 is external to the SOC.

Implementation on Commercial Multicore Architectures

The efficient algorithm of present invention is capable of implementation on many commercial parallel systems, which offer advantages of lower cost and mature programming environment, such as many-core architecture of Larrabee by Intel, Cell by IBM, and others. In this class of architectures each core PE has its own cache memory element, and the multiple processing cores are connected via ring bus. Each PE entirely depends on the ring to transfer data to and from the other caches. In these transfers the number of steps involved in sending the packet has typically very little impact on transfer latency: the clock speed driving the steps is very fast relative to other considerations. However, longer communication distances are detrimental to the overall performance of the ring bus as they reduce available concurrency. Such a reduced concurrency is an inhibiting factor for prior art's space division ray-tracing. As opposed to it, the modular space division of present invention is an enabler to space division ray-tracing by replacing the majority of long communication distances, or global communication, by short data exchanges between neighboring PEs, drastically rising the concurrency of ring communication.

Larrabee by Intel is an example of many-core architecture with coherent caching, designed around fast ring connecting X86 based CPUs with local caches, as shown in block diagram of FIG. 12. Due to coherency, all local caches can be seen as one global 2nd level (L2) cache, divided into separate local subsets, one per CPU core. Each CPU has a fast direct access path to its own local subset of the L2 cache. Data read by a CPU core is stored in its L2 cache subset and can be accessed quickly, in parallel with other CPUs accessing their own local L2 cache subsets. Data written by a CPU core is stored in its own L2 cache subset and is flushed to other subsets, if necessary. The ring network ensures coherency for shared data.

Implementation of the present invention's method on Larrabee is done by dividing the scene space among the core CPUs, each CPU controlling N2 cells. The monitoring is done either by one of the cores functioning as privileged CPU, or by an external CPU. Local cache holds the cell's data of all assigned cells, in a turn. Moving ray record to other CPUs is done by writing to its local L2 cache subset via ring bus. Data flows stepwise around the ring. Data exchange between two physically neighboring CPUs can be done concurrently with any other pair of CPUs. However, longer communication distances are harmful to the overall performance of the ring as they reduce available concurrency. Therefore it is of great advantage to reduce the communication to short neighboring segments along the ring, by applying the modular mapping of present invention. The four arrows in FIG. 12 indicate the communication of the example in FIG. 9A, implemented on Larrabee.

Another commercial multi-core architecture is Cell by IBM (see FIG. 13). Each Cell consists of a PowerPC core (PPE) and eight “synergetic co-processor elements” (SPEs). Each SPE has 256 KB local store, which is incoherent with the rest of the memory system, a memory flow controller (MFC) and synergetic processing unit (SPU). The EIB bus is responsible of transferring data between SPEs. It is implemented as a circular ring comprising four unidirectional channels which counter-rotate in pairs. Each SPE has one read port and one write port, and a dedicated DMA management queue capable of transactions to various endpoints without interfering with the ongoing computations. Each SPE depends on DMA to transfer data to and from the main memory and other SPE's local memories. Data flows on an EIB channel stepwise around the ring.

Implementation of present invention on Cell is rather straightforward, e.g. the PPE monitors the entire process while the scene space is partitioned among and controlled by the SPEs. Local stores hold the data associated with cells. Each SPE has access to local stores of other SPEs via EIB bus.

Although several exemplary embodiments have been herein shown and described, those of skill in the art will recognize that many modifications and variations are possible without departing from the spirit and scope of the invention, and it is intended to measure the invention only by the appended claims. 

What is claimed is:
 1. A multiprocessing computing system having reduced inter-processor communication, comprising: an array of at least three processing elements interconnected by a ring topology; a three-dimensional data; a three-dimensional uniform grid of cells; wherein said data is mapped onto said grid of cells by way of modular mapping scheme; and each cell is assigned a processing element, according to said modular mapping scheme; and each cell exchanges said elements of said data with its 6-connected neighboring cells; thereby, when a cell is processed by its assigned processing element, all the data with its 6-connected neighboring cells is interchanged by the cells' assigned processors through at most one hop along the ring, representing a shortest physical path between the cells.
 2. The computing system of claim 1, wherein the exchange of data by a cell needs not be synchronized with its geographically neighboring cells.
 3. The computing system of claim 1, wherein said exchange of data from a sender-cell to a receiver-cell, is performed by the processing element designated to the sender-cell.
 4. The computing system of claim 1, wherein said processing element designated to a cell is in a communication distance of at most one ring hop to processing element assigned to any of the cell's six connected neighboring cells.
 5. A method of reducing inter-processor communication in a multiprocessing system of at least three processing elements, wherein a processed data three-dimensional, comprising the steps of: connecting said processing elements by a ring interconnection; partitioning the data onto a uniform grid of cells; mapping said cells, according to a modular mapping scheme; assigning the processing elements to said cells, according to the modular mapping scheme; thereby a modularly mapped cell exchanges data with each of its 6-connected neighboring cells via a shortest physical path consisting of at most one hop along the ring.
 6. The method of claim 5, wherein the exchange of data needs not be synchronized by the cell with its geographically neighboring cells.
 7. The method of claim 5, wherein said exchange of data from a sender-cell to a receiver-cell is performed by the processing element assigned to the sender-cell.
 8. The method of claim 5, wherein said processing element assigned to a cell, is in a communication distance of at most one ring hop to processing element assigned to any of the cell's six connected neighboring cells. 